View Profile - Profiles - UT Arlington. L. Incremental Query Processing on Big Data Streams. IEEE Transactions on Knowledge and Data Engineering, 2. A Query Processing Framework for Array- Based Computations. Incremental Stream Processing of Nested- Relational Queries. Distributed Incremental Graph Analysis. Big. Data Congress, June 2. New Ideas Track: Testing MapReduce-Style Programs Christoph Csallner [email protected] Leonidas Fegaras [email protected] Chengkai Li [email protected] Computer Science and Engineering Department University of Texas at Arlington. Profile Search; Browse; Access Your Profile. Testing MapReduce-style programs. By Christoph Csallner, Leonidas Fegaras, and Chengkai Li. 19th ACM SIGSOFT Symposium on the Foundations of Software. Manimal: Relational Optimization for Data-Intensive Programs. MapReduce program testing: a systematic mapping study. Using Map. Reduce to Speed Up Storm Identification from Big Raw Rainfall Data. The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, in Computation Tools 2. Valencia, Spain, June 2. Complete Storm Identification Algorithms from Big Raw Rainfall Data Using Map. Reduce Framework. Workshop on Big Data and Science: Infrastructure and Services, in IEEE Big. Data'1. 3, Silicon Valley, CA, October 2. Map- Based Graph Analysis on Map. Reduce, 2. 01. 3 IEEE International Conference on Big Data (Big. Data'1. 3), Silicon Valley, CA, October 2. Hadoop self learning,hadoop projects,hadoop online training,hadoop online tutorial,hadoop training classes. MRFlow is applied over some MapReduce programs and detects several defects. An Optimization Framework for Map- Reduce Queries. March 2. 01. 2. 1. International Conference on Extending Database Technology (EDBT'1. Berlin, Germany. Conference Paper. Published. 20. 12. R. A Load Shedding Framework for Processing Top- k Join Aggregation Queries. July 2. 01. 2. 3rd International Workshop on Database Management Systems (DMS'1. Zebra includes several classes for use in MapReduce programs. The main entry point into Zebra are the two classes for reading and writing tables, namely TableInputFormat and BasicTableOutputFormat.Chennai, India. Conference Paper. Published. 20. 12. S. Multi Person Identification and Localization in Pervasive Assistive Environments. International Journal of Computer Science and Management Research (IJCSMR), 1(4): 7. Delivering QOS in XML Data Stream Processing Using Load Shedding International Journal of Database Management Systems (IJDMS), 4(3), 4. Supporting Bulk Synchronous Parallelism in Map- Reduce Queries. International Workshop on Data Intensive Computing in the Clouds (Data. Cloud'1. 2), Salt Lake City, Utah, November 2. An Adaptable Framework for Integrating and Querying Sensor Data. Artificial Intelligence Applications in Biomedicine (AIAB 2. Corfu, Greece, September 2. Conference Paper. Published. 20. 11. C. Testing Map. Reduce- Style Programs. ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE'1. Szeged Hungary, September 2. Conference Paper. Published. 20. 11. A. A Scalable and Self- Adapting Notification Framework for Healthcare. Information Systems. International Journal Universal Access in the Information Society (UAIS), special issue on Pervasive Technologies and Assistive Environments: Social Impact, Financial, Government and Privacy Issues. Fegaras, A Okorodudu and D. Testing Map. Reduce with MRUnit - DZone Big Data. Hortonworks Data. Flow is an integrated platform that makes data ingestion fast, easy, and secure. Download the white paper now. Testing and debugging multi threaded programs is hard. Now take the same programs and massively distribute them across multiple JVMs deployed on a cluster of machines and the complexity goes off the roof. One way to overcome this complexity is to do testing in isolation and catch as many bugs as possible locally. MRUnit is a testing framework that lets you test and debug Map Reduce jobs in isolation without spinning up a Hadoop cluster. In this blog post we will cover various features of MRUnit by walking through a simple Map. Reduce job. Lets say we want to take the input below and create an inverted index using Map. Reduce. Inputwww. Expected outputantiques www. Mapper and Reducer that do the transformationpublic class Inverted. Index. Mapper extends Map. Reduce. Base implements Mapper< Long. Writable, Text, Text, Text> . For example line retailer,category. Reducer gets a key and a list of values, transforms the list of values to a comma delimited String and emits the key and value out. Now lets use MRUnit to write various tests for this Job. Three key classes in MRUnits are Map. Driver for Mapper Testing, Reduce. Driver for Reducer Testing and Map. Reduce. Driver for end to end Map. Reduce Job testing. This is how we will setup the Test Class. Inverted. Index. Job. Test . First style is to tell the framework both input and output values and let the framework do the assertions, second is the more traditional approach where you do the assertion yourself. Lets write a test using the first approach.@Test. Mapper. With. Single. Key. And. Value() throws Exception . This test can be written in a more traditional way as follow@Test. Mapper. With. Single. Key. And. Value. With. Assertion() throws Exception . MRUnit provides a fluent API to support this use case. Here is an example@Test. Mapper. With. Single. Input. And. Multiple. Output() throws Exception . You can also pass multiple key value pairs as input to your job. Test below demonstrate Map. Reduce. Driver in action@Test. Map. Reduce() throws Exception.
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